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MongoDB索引类型汇总分享

数据库 作者： 那海蓝蓝
目录
  • 单字段索引
    • 在单个字段上创建升序索引
    • 在嵌入式字段上创建索引
    • 在内嵌文档上创建索引
  • 复合索引
    • 多键索引
      • 文本索引
        • 2dsphere索引
          • 创建测试数据
          • 添加2dsphere索引
          • 利用2dsphere索引查询多边形里的点
          • 利用2dsphere索引查询球体上定义的圆内的点
        • 2d索引
          • 哈希索引

            MongoDB 4.2官方支持索引类型如下：

            • 单字段索引
            • 复合索引
            • 多键索引
            • 文本索引
            • 2dsphere索引
            • 2d索引
            • geoHaystack索引
            • 哈希索引

            单字段索引

            在单个字段上创建升序索引

            hand编程客栈ong1:PRIMARY> db.test.getIndexes()
[
	{
		"v" : 2,
		"key" : {
			"_id" : 1
		},
		"name" : "_id_",
		"ns" : "db6.test"
	}
]

            在字段id上添加升序索引

            handong1:PRIMARY> db.test.createIndex({"id":1})
{
	"createdCollectionAutomatically" : false,
	"numIndexesBefore" : 1,
	"numIndexesAfter" : 2,
	"ok" : 1,
	"$clusterTime" : {
		"clusterTime" : Timestamp(1621322378, 1),
		"signature" : {
			"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
			"keyId" : NumberLong(0)
		}
	},
	"operationTime" : Timestamp(1621322378, 1)
}


            handong1:PRIMARY> db.test.getIndexes()
[
	{
		"v" : 2,
		"key" : {
			"_id" : 1
		},
		"name" : "_id_",
		"ns" : "db6.test"
	},
	{
		"v" : 2,
		"key" : {
			"id" : 1
		},
		"name" : "id_1",
		"ns" : "db6.test"
	}
]


            handong1:PRIMARY> db.test.find({"id":100})
{ "_id" : ObjectId开发者_Kafka("60a35d061f183b1d8f092114"), "id" : 100, "name" : "handong", "ziliao" : { "name" : "handong", "age" : 25, "hobby" : "mongodb" laFlSjTBv} }

            上述查询可以使用新建的单字段索引。

            在嵌入式字段上创建索引

            handong1:PRIMARY> db.test.createIndex({"ziliao.name":1})
{
	"createdCollectionAutomatically" : false,
	"numIndexesBefore" : 2,
	"numIndexesAfter" : 3,
	"ok" : 1,
	"$clusterTime" : {
		"clusterTime" : Timestamp(1621323677, 2),
		"signature" : {
			"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
			"keyId" : NumberLong(0)
		}
	},
	"operationTime" : Timestamp(1621323677, 2)
}

            以下查询可以用的新建的索引。

            db.test.find({"ziliao.name":"handong"})

            在内嵌文档上创建索引

            handong1:PRIMARY> db.test.createIndex({ziliao:1})
{
	"createdCollectionAutomatically" : false,
	"numIndexesBefore" : 3,
	"numIndexesAfter" : 4,
	"ok" : 1,
	"$clusterTime" : {
		"clusterTime" : Timestamp(1621324059, 2),
		"signature" : {
			"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
			"keyId" : NumberLong(0)
		}
	},
	"operationTime" : Timestamp(1621324059, 2)
}

            以下查询可以使用新建的索引。

            db.test.find({ziliao:{ "name" : "handong", "age" : 25, "hobby" : "mongodb" }})

            复合索引

            创建复合索引

            db.user.createIndex({"product_id":1,"type":-1})

            以下查询可以用到新建的复合索引

            db.user.find({"product_id":"e5a35cfc70364d2092b8f5d14b1a3217","type":0})

            多键索引

            基于一个数组创建索引，MongoDB会自动创建为多键索引，无需刻意指定。

            多键索引也可以基于内嵌文档来创建。

            多键索引的边界值的计算依赖于特定的规则。

            查看文档：

            handong1:PRIMARY> db.score.find()
{ "_id" : ObjectId("60a32d7f1f183b1d8f0920ad"), "name" : "dandan", "age" : 30, "score" : [ { "english" : 90, "math" : 99, "physics" : 88 } ], "is_del" : false }
{ "_id" : ObjectId("60a32d8b1f183b1d8f0920ae"), "name" : "dandan", "age" : 30, "score" : [ 99, 98, 97, 96 ], "is_del" : false }
{ "_id" : ObjectId("60a32d9a1f183b1d8f0920af"), "name" : "dandan", "age" : 30, "score" : [ 100, 100, 100, 100 ], "is_del" : false }
{ "_id" : ObjectId("60a32e8c1f183b1d8f0920b0"), "name" : "dandan", "age" : 30, "score" : [ { "english" : 70, "math" : 99, "physics" : 88 } ], "is_del" : false }
{ "_id" : ObjectId("60a37b141f183b1d8f0aa751"), "name" : "dandan", "age" : 30, "score" : [ 96, 95 ] }
{ "_id" : ObjectId("60a37b1d1f183b1d8f0aa752"), "name" : "dandan", "age" : 30, "score" : [ 96, 95, 94 ] }
{ "_id" : ObjectId("60a37b221f183b1d8f0aa753"), "name" : "dandan", "age" : 30, "score" : [ 96, 95, 94, 93 ] }

            创建score字段多键索引：

            db.score.createIndex("score":1)

            handong1:PRIMARY> db.score.find({"score":[ 96, 95 ]})
{ "_id" : ObjectId("60a37b141f183b1d8f0aa751"), "name" : "dandan", "age" : 30, "score" : [ 96, 95 ] }

            查看执行计划：

            handong1:PRIMARY> db.score.find({"score":[ 96, 95 ]}).explain()
{
	"queryPlanner" : {
		"plannerVersion" : 1,
		"namespace" : "db6.score",
		"indexFilterSet" : false,
		"parsedQuery" : {
			"score" : {
				"$eq" : [
					96,
					95
				]
			}
		},
		"queryHash" : "8D76FC59",
		"planCacheKey" : "E2B03CA1",
		"winningPlan" : {
			"stage" : "FETCH",
			"filter" : {
				"score" : {
					"$eq" : [
						96,
						95
					]
				}
			},
			"inputStage" : {
				"stage" : "IXSCAN",
				"keyPattern" : {
					"score" : 1
				},
				"indexName" : "score_1",
				"isMultiKey" : true,
				"multiKeyPaths" : {
					"score" : [
						"score"
					]
				},
				"isUnique" : false,
				"isSparse" : false,
				"isPartial" : false,
				"indexVersion" : 2,
				"direction" : "forward",
				"indexBounds" : {
					"score" : [
						"[96.0, 96.0]",
						"[[ 96.0, 95.0 ], [ 96.0, 95.0 ]]"
					]
				}
			}
		},
		"rejectedPlans" : [ ]
	},
	"serverInfo" : {
		"host" : "mongo3",
		"port" : 27017,
		"version" : "4.2.12",
		"gitVersion" : "5593fd8e33b60c75802edab304e23998fa0ce8a5"
	},
	"ok" : 1,
	"$clusterTime" : {
		"clusterTime" : Timestamp(1621326912, 1),
		"signature" : {
			"hash" : BinData(0,"AAAAhttp://www.devze.comAAAAAAAAAAAAAAAAAAAAAAA="),
			"keyId" : NumberLong(0)
		}
	},
	"operationTime" : Timestamp(1621326912, 1)
}

            可以看到已经使用了新建的多键索引。

            文本索引

                为了支持对字符串内容的文本搜索查询，MongoDB提供了文本索引。文本(text )索引可以包含任何值为字符串或字符串元素数组的字段

            db.user.createIndex({"sku_attributes":"text"})

            db.user.find({$text:{$search:"测试"}})

            查看执行计划：

            handong1:PRIMARY> db.user.find({$text:{$search:"测试"}}).explain()
{
	"queryPlanner" : {
		"plannerVersion" : 1,
		"namespace" : "db6.user",
		"indexFilterSet" : false,
		"parsedQuery" : {
			"$text" : {
				"$search" : "测试",
				"$language" : "english",
				"$caseSensitive" : false,
				"$diacriticSensitive" : false
			}
		},
		"queryHash" : "83098EE1",
		"planCacheKey" : "7E2D582B",
		"winningPlan" : {
			"stage" : "TEXT",
			"indexPrefix" : {
				
			},
			"indexName" : "sku_attributes_text",
			"parsedTextQuery" : {
				"terms" : [
					"测试"
				],
				"negatedTerms" : [ ],
				"phrases" : [ ],
				"negatedPhrases" : [ ]
			},
			"textIndexVersion" : 3,
			"inputStage" : {
				"stage" : "TEXT_MATCH",
				"inputStage" : {
					"stage" : "FETCH",
					"inputStage" : {
						"stage" : "OR",
						"inputStage" : {
							"stage" : "IXSCAN",
							"keyPattern" : {
								"_fts" : "text",
								"_ftsx" : 1
							},
							"indexName" : "sku_attributes_text",
							"isMultiKey" : true,
							"isUnique" : false,
							"isSparse" : false,
							"isPartial" : false,
							"indexVersion" : 2,
							"direction" : "backward",
							"indexBounds" : {
								
							}
						}
					}
				}
			}
		},
		"rejectedPlans" : [ ]
	},
	"serverInfo" : {
		"host" : "mongo3",
		"port" : 27017,
		"version" : "4.2.12",
		"gitVersion" : "5593fd8e33b60c75802edab304e239laFlSjTBv98fa0ce8a5"
	},
	"ok" : 1,
	"$clusterTime" : {
		"clusterTime" : Timestamp(1621328543, 1),
		"signature" : {
			"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
			"keyId" : NumberLong(0)
		}
	},
	"operationTime" : Timestamp(1621328543, 1)
}

            可以看到通过文本索引可以查到包含测试关键字的数据。

            **注意：**可以根据自己需要创建复合文本索引。

            2dsphere索引

            创建测试数据

            db.places.insert(
   {
      loc : { type: "Point", coordinates: [ 116.291226, 39.981198 ] },
      name: "火器营桥",
      category : "火器营桥"
   }
)


db.places.insert(
   {
      loc : { type: "Point", coordinates: [ 116.281452, 39.914226 ] },
      name: "五棵松",
      category : "五棵松"
   }
)

db.places.insert(
   {
      loc : { type: "Point", coordinates: [ 116.378038, 39.851467 ] },
      name: "角门西",
      category : "角门西"
   }
)


db.places.insert(
   {
      loc : { type: "Point", coordinates: [ 116.467833, 39.881581 ] },
      name: "潘家园",
      category : "潘家园"
   }
)

db.places.insert(
   {
      loc : { type: "Point", coordinates: [ 116.468264, 39.914766 ] },
      name: "国贸",
      category : "国贸"
   }
)

db.places.insert(
   {
      loc : { type: "Point", coordinates: [ 116.46618, 39.960213 ] },
      name: "三元桥",
      category : "三元桥"
   }
)

db.places.insert(
   {
      loc : { type: "Point", coordinates: [ 116.400064, 40.007827 ] },
      name: "奥林匹克森林公园",
      category : "奥林匹克森林公园"
   }
)

            添加2dsphere索引

            db.places.createIndex( { loc : "2dsphere" } )


            db.places.createIndex( { loc : "2dsphere" , category : -1, name: 1 } )

            利用2dsphere索引查询多边形里的点

            凤凰岭

            [116.098234,40.110569]

            天安门

            [116.405239,39.913839]

            四惠桥

            [116.494351,39.912068]

            望京

            [116.494494,40.004594]

            handong1:PRIMARY> db.places.find( { loc :
...                   { $geoWithin :
...                     { $geometry :
...                       { type : "Polygon" ,
...                         coordinates : [ [
...                                           [116.098234,40.110569] ,
...                                      编程客栈     [116.405239,39.913839] ,
...                                           [116.494351,39.912068] ,
...                                           [116.494494,40.004594] ,
...                                           [116.098234,40.110569]
...                                         ] ]
...                 } } } } )
{ "_id" : ObjectId("60a4c950d4211a77d22bf7f8"), "loc" : { "type" : "Point", "coordinates" : [ 116.400064, 40.007827 ] }, "name" : "奥林匹克森林公园", "category" : "奥林匹克森林公园" }
{ "_id" : ObjectId("60a4c94fd4211a77d22bf7f7"), "loc" : { "type" : "Point", "coordinates" : [ 116.46618, 39.960213 ] }, "name" : "三元桥", "category" : "三元桥" }
{ "_id" : ObjectId("60a4c94fd4211a77d22bf7f6"), "loc" : { "type" : "Point", "coordinates" : [ 116.468264, 39.914766 ] }, "name" : "国贸", "category" : "国贸" }

            可以看到把集合中包含在指定四边形里的点，全部列了出来。

            利用2dsphere索引查询球体上定义的圆内的点

            handong1:PRIMARY> db.places.find( { loc :
...                   { $geoWithin :
...                     { $centerSphere :
...                        [ [ 116.439518, 39.954751 ] , 2/3963.2 ]
...                 } } } )
{ "_id" : ObjectId("60a4c94fd4211a77d22bf7f7"), "loc" : { "type" : "Point", "coordinates" : [ 116.46618, 39.960213 ] }, "name" : "三元桥", "category" : "三元桥" }

            返回所有半径为经度 116.439518 E 和纬度 39.954751 N 的2英里内坐标。示例将2英里的距离转换为弧度，通过除以地球近似的赤道半径3963.2英里。

            2d索引

            在以下情况下使用2d索引：

            • 您的数据库具有来自MongoDB 2.2或更早版本的旧版旧版坐标对。
            • 您不打算将任何位置数据存储为GeojsON对象。

            哈希索引

            要创建hashed索引，请指定 hashed 作为索引键的值，如下例所示：

            handong1:PRIMARY> db.test.createIndex({"_id":"hashed"})
{
	"createdCollectionAutomatically" : false,
	"numIndexesBefore" : 4,
	"numIndexesAfter" : 5,
	"ok" : 1,
	"$clusterTime" : {
		"clusterTime" : Timestamp(1621419338, 1),
		"signature" : {
			"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
			"keyId" : NumberLong(0)
		}
	},
	"operationTime" : Timestamp(1621419338, 1)
}

            注意事项

            • MongoDB支持任何单个字段的 hashed 索引。hashing函数折叠嵌入的文档并计算整个值的hash值，但不支持多键(即.数组)索引。
            • 您不能创建具有hashed索引字段的复合索引，也不能在索引上指定唯一约束hashed；但是，您可以hashed在同一字段上创建索引和升序/降序（即非哈希）索引：MongoDB将对范围查询使用标量索引。

             到此这篇关于MongoDB索引类型汇总分享的文章就介绍到这了,更多相关MongoDB索引内容请搜索我们以前的文章或继续浏览下面的相关文章希望大家以后多多支持我们！

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